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  <h1>optuna.importance._mean_decrease_impurity 源代码</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">ValuesView</span>

<span class="kn">import</span> <span class="nn">numpy</span>

<span class="kn">from</span> <span class="nn">optuna._experimental</span> <span class="kn">import</span> <span class="n">experimental</span>
<span class="kn">from</span> <span class="nn">optuna._imports</span> <span class="kn">import</span> <span class="n">try_import</span>
<span class="kn">from</span> <span class="nn">optuna.distributions</span> <span class="kn">import</span> <span class="n">BaseDistribution</span>
<span class="kn">from</span> <span class="nn">optuna.distributions</span> <span class="kn">import</span> <span class="n">CategoricalDistribution</span>
<span class="kn">from</span> <span class="nn">optuna.importance._base</span> <span class="kn">import</span> <span class="n">_get_distributions</span>
<span class="kn">from</span> <span class="nn">optuna.importance._base</span> <span class="kn">import</span> <span class="n">_get_study_data</span>
<span class="kn">from</span> <span class="nn">optuna.importance._base</span> <span class="kn">import</span> <span class="n">BaseImportanceEvaluator</span>
<span class="kn">from</span> <span class="nn">optuna.study</span> <span class="kn">import</span> <span class="n">Study</span>

<span class="k">with</span> <span class="n">try_import</span><span class="p">()</span> <span class="k">as</span> <span class="n">_imports</span><span class="p">:</span>
    <span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
    <span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
    <span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">OneHotEncoder</span>


<div class="viewcode-block" id="MeanDecreaseImpurityImportanceEvaluator"><a class="viewcode-back" href="../../../reference/importance.html#optuna.importance.MeanDecreaseImpurityImportanceEvaluator">[文档]</a><span class="nd">@experimental</span><span class="p">(</span><span class="s2">&quot;1.5.0&quot;</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">MeanDecreaseImpurityImportanceEvaluator</span><span class="p">(</span><span class="n">BaseImportanceEvaluator</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Mean Decrease Impurity (MDI) parameter importance evaluator.</span>

<span class="sd">    This evaluator fits a random forest that predicts objective values given hyperparameter</span>
<span class="sd">    configurations. Feature importances are then computed using MDI.</span>

<span class="sd">    .. note::</span>

<span class="sd">        This evaluator requires the `sklean &lt;https://scikit-learn.org/stable/&gt;`_ Python package and</span>
<span class="sd">        is based on `sklearn.ensemble.RandomForestClassifier.feature_importances_</span>
<span class="sd">        &lt;https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.feature_importances_&gt;`_.</span>

<span class="sd">    Args:</span>
<span class="sd">        n_estimators:</span>
<span class="sd">            Number of trees in the random forest.</span>
<span class="sd">        max_depth:</span>
<span class="sd">            The maximum depth of each tree in the random forest.</span>
<span class="sd">        random_seed:</span>
<span class="sd">            Seed for the random forest.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span> <span class="n">n_estimators</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">16</span><span class="p">,</span> <span class="n">max_depth</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">random_state</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">_imports</span><span class="o">.</span><span class="n">check</span><span class="p">()</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">_forest</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span>
            <span class="n">n_estimators</span><span class="o">=</span><span class="n">n_estimators</span><span class="p">,</span>
            <span class="n">max_depth</span><span class="o">=</span><span class="n">max_depth</span><span class="p">,</span>
            <span class="n">min_samples_split</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
            <span class="n">min_samples_leaf</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span>
        <span class="p">)</span>

<div class="viewcode-block" id="MeanDecreaseImpurityImportanceEvaluator.evaluate"><a class="viewcode-back" href="../../../reference/importance.html#optuna.importance.MeanDecreaseImpurityImportanceEvaluator.evaluate">[文档]</a>    <span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">study</span><span class="p">:</span> <span class="n">Study</span><span class="p">,</span> <span class="n">params</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]])</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
        <span class="n">distributions</span> <span class="o">=</span> <span class="n">_get_distributions</span><span class="p">(</span><span class="n">study</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
        <span class="n">params_data</span><span class="p">,</span> <span class="n">values_data</span> <span class="o">=</span> <span class="n">_get_study_data</span><span class="p">(</span><span class="n">study</span><span class="p">,</span> <span class="n">distributions</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">params_data</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>  <span class="c1"># `params` were given but as an empty list.</span>
            <span class="k">return</span> <span class="n">OrderedDict</span><span class="p">()</span>

        <span class="n">params_data</span><span class="p">,</span> <span class="n">cols_to_raw_cols</span> <span class="o">=</span> <span class="n">_encode_categorical</span><span class="p">(</span><span class="n">params_data</span><span class="p">,</span> <span class="n">distributions</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>

        <span class="n">forest</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forest</span>
        <span class="n">forest</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">params_data</span><span class="p">,</span> <span class="n">values_data</span><span class="p">)</span>
        <span class="n">feature_importances</span> <span class="o">=</span> <span class="n">forest</span><span class="o">.</span><span class="n">feature_importances_</span>
        <span class="n">feature_importances_reduced</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">distributions</span><span class="p">))</span>
        <span class="n">numpy</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">at</span><span class="p">(</span><span class="n">feature_importances_reduced</span><span class="p">,</span> <span class="n">cols_to_raw_cols</span><span class="p">,</span> <span class="n">feature_importances</span><span class="p">)</span>

        <span class="n">param_importances</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
        <span class="n">param_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">distributions</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">feature_importances_reduced</span><span class="o">.</span><span class="n">argsort</span><span class="p">()[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
            <span class="n">param_importances</span><span class="p">[</span><span class="n">param_names</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="n">feature_importances_reduced</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>

        <span class="k">return</span> <span class="n">param_importances</span></div></div>


<span class="k">def</span> <span class="nf">_encode_categorical</span><span class="p">(</span>
    <span class="n">params_data</span><span class="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">distributions</span><span class="p">:</span> <span class="n">ValuesView</span><span class="p">[</span><span class="n">BaseDistribution</span><span class="p">],</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]:</span>
    <span class="c1"># Transform the `params_data` matrix by expanding categorical integer-valued columns to one-hot</span>
    <span class="c1"># encoding matrices. Note that the resulting matrix can be sparse and potentially very big.</span>

    <span class="n">numerical_cols</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">categorical_cols</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">categories</span> <span class="o">=</span> <span class="p">[]</span>

    <span class="k">for</span> <span class="n">col</span><span class="p">,</span> <span class="n">distribution</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">distributions</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">distribution</span><span class="p">,</span> <span class="n">CategoricalDistribution</span><span class="p">):</span>
            <span class="n">categorical_cols</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
            <span class="n">categories</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">distribution</span><span class="o">.</span><span class="n">choices</span><span class="p">))))</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">numerical_cols</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
    <span class="k">assert</span> <span class="n">col</span> <span class="o">==</span> <span class="n">params_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span>

    <span class="n">col_transformer</span> <span class="o">=</span> <span class="n">ColumnTransformer</span><span class="p">(</span>
        <span class="p">[(</span><span class="s2">&quot;_categorical&quot;</span><span class="p">,</span> <span class="n">OneHotEncoder</span><span class="p">(</span><span class="n">categories</span><span class="o">=</span><span class="n">categories</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span> <span class="n">categorical_cols</span><span class="p">)],</span>
        <span class="n">remainder</span><span class="o">=</span><span class="s2">&quot;passthrough&quot;</span><span class="p">,</span>
    <span class="p">)</span>
    <span class="c1"># All categorical one-hot columns are placed before the numerical columns in</span>
    <span class="c1"># `ColumnTransformer.fit_transform`.</span>
    <span class="n">params_data</span> <span class="o">=</span> <span class="n">col_transformer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">params_data</span><span class="p">)</span>

    <span class="c1"># `cols_to_raw_cols[&quot;column index in transformed matrix&quot;]</span>
    <span class="c1">#     == &quot;column index in original matrix&quot;`</span>
    <span class="n">cols_to_raw_cols</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">params_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>

    <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">categorical_col</span><span class="p">,</span> <span class="n">cats</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">categorical_cols</span><span class="p">,</span> <span class="n">categories</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cats</span><span class="p">)):</span>
            <span class="n">cols_to_raw_cols</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">categorical_col</span>
            <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">for</span> <span class="n">numerical_col</span> <span class="ow">in</span> <span class="n">numerical_cols</span><span class="p">:</span>
        <span class="n">cols_to_raw_cols</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">numerical_col</span>
        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">assert</span> <span class="n">i</span> <span class="o">==</span> <span class="n">cols_to_raw_cols</span><span class="o">.</span><span class="n">size</span>

    <span class="k">return</span> <span class="n">params_data</span><span class="p">,</span> <span class="n">cols_to_raw_cols</span>
</pre></div>

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